1. The Leeds Data Model Past, Present and Future
Will Ridge and Chris Charlton
October 3rd 2017

2. Will Ridge Chris Charlton
Integrated Business Intelligence Manager, Leeds City Council and Leeds CCGs
Chris Charlton
Senior Information Manager, Leeds CCGs and Leeds City Council

3. Valuing Business Intelligence
Senior Leadership and Vision within Informatics and recognising the value of BI to inform decision making and whole systems transformation
Use of joined up information and analytics across the city to provide knowledge and insights into how effective health and care processes are
Intelligent analysis to enable the health and care system to evaluate the impact of changes, identify opportunities for change and support outcome based commissioning

4. Leeds Health and Care Hub
LA Chief Digital Lead
CCG Director of Informatics
Head of IM&T
& Technology
Chief Analyst – City wide
CCG and GP IT Team
Public Health
Intelligence Team
Integrated Bi Manager
Business Partner and
Data Quality and IG Team
Information Manager – City wide
CCG Partnership
Business Intelligence Team
Business Intelligence functions of the Leeds CCGs Partnership and Leeds City Council Public Health Intelligence Teams combined to form a single intelligence function.

6. Example of the Leeds Data Model
Single line- data set

7. Example of the Leeds Data Model
Single line- data set

8. Example of the Leeds Data Model
All lines- linked data

9. Example of the Leeds Data Model

10. Caretrak

11. Caretrak

12. Existing LDM ASC Activity Files ASC Contact Referrals Assessments
Reablement
Service Provision
Reviews
DSCRO
ASC Activity Files
Outpatients
A&E Attendances
Leeds Community Health
Community Mental Health
CIC Beds
GP Data
Inpatients
Leeds Data Model
Various Risk Models
Project Specific Data
Population Health Management Data?
Person Identifiable Data
Pseudonymised Data
Activity Data no PID
Data Processor
Project Specific Data

13. IG Information Governance challenges:
Original model struck in now five years old;
IG needs to be updated;
GDPR.
Work with NHS digital to develop a more sustainable model;
Resulting model can be developed and rolled out- though IG challenges remain

14. Requirement for new IT infrastructure;
Failure of existing infrastructure;
Development of a new, more sustainable approaches- move away from laptop lead activity, toward server level.

15. Restructure of the service;
Larger influence of public health and re-orientation of the data model.

16. Leeds One City – diverse outcomes
(iii) Analytics to identify inequalities – deprived v non deprived ; profiles; complexities of Leeds; two populations
Note ‘Deprived Leeds cohort’ also over x2 England average living in areas ranking in top 10% most deprived nationally
Increasing polarisation of deprivation in Leeds
Analyses by PH Intelligence and other Intelligence departments of the Council suggest that the latest IMD shows a polarisation of the deprivation in Leeds, which we might expect to increase the health inequalities gap. This appears to be happening (see next slide).

17. Leeds Health and Care System – Macro Population Segmentation Model
Life course
& disease progression
9th category of episodic care
Holistic person centred care, in each cohort service delivery would need to address inequalities, social, mental and emotional wellbeing, mental and physical health

18. Leeds data model activity and cost by per capita
COST to the system
Leeds data model activity and cost by per capita
End of life care then frailty
Also “system shocks” due to frailty
Note: missing data sets, Ambulance, MH (ex Community MH)

19. Further Developments
Labouring the metaphor from earlier, we want to improve the Leeds Data Model to include more data about context, populations and the bigger picture;
Including working with health and care professionals;
Orientating with other data sets;
Improved population health management- closing the gap between the deprived and none deprived communities

20. Kent Integrated Dataset (KID)
Dr Abraham P George
Consultant in Public Health

21. Context
Changing population - impacts on all care sectors with more to come as the epidemic takes hold eg. obesity, diabetes, alcohol, frailty
Huge NHS and public sector funding gap
Public sector services expected to discharge statutory functions with ever shrinking budgets
Growing need for ‘systems thinking’ - how money and resources are being utilised for population health and wellbeing
Better understanding role of public authorities in new models of care

22. Importance of question setting
Modelling and simulation for capacity planning
Predictive modelling / risk stratification
Population segmentation / capitation budgets
Complex care evaluation - matched controlled analyses

23. Current limitations in ‘systems’ planning
Datasets are anonymised, cannot join one dataset to another
Difficulty in understanding the ‘patient’ or ‘citizen’ journey when and how they are using services and their costs
Exploring cause / effect relationship between one indicator and another difficult
Many factors and services affecting population health. How can we estimate the relative impact of each?
Modelling demand is not ‘linear’ - lots of assumptions are used. Are they correct?
Limited benefit to predict growth and demand for health and care services
Business cases for investing in new care models and services also difficult

24. The story so far Started 4 years ago as national pilot
KCC Public Health works closely with local data warehouse team that collates and link data from >160 organisations. >50 million patient records to date
Minimal cost
Almost 20 analytical projects carried out supporting local health and care commissioning including Kent & Medway STP
5 times finalist for HSJ and LGC awards
Interest from academic organisations who want to work with us for analyse data to help towards planning

25. Our vision
Development & design of whole population person level linked dataset for Kent
Working in partnership with CCGs to support local and national health and care service planning
Map and link person level data not just from NHS and adult social but also other public authorities – Fire & Rescue, districts, education etc.
Complex analytics to support evidence based investment and disinvestment
Harness skills and expertise from local and national intelligence teams to improve quality of the data
Develop and use innovative solutions around information technology, data protection and statistical and modelling tools

26. KENT INTEGRATED DATASET
Flow of data into the Kent Integrated Dataset
GP practice
Community health
Mental health
Out of hours
Acute hospital
Public health
Adult social care
Ambulance service
Hospice
KID minimum dataset: data on activity, cost, service/treatment received, staffing, commissioning and providing organisation, patient diagnosis, demographics and location.
Datasets linked on a common patient identifier (NHS number) and pseudonymised
UPRN also used to link at household level
KENT INTEGRATED DATASET
Kent County Council Public Health and HISBI data warehouse
Arrangements are in progress to link to data covering other services, including:
Health and social care services: Children’s social care, child and adolescent mental health, improving access to psychological therapies, and non-SUS-reported acute care.
Non-health and social care services: District council, HM Prisons, Fire and Rescue, Probation, and Education.

27. KENT INTEGRATED DATASET Data Quality Improvement
Uses of the Kent Integrated Dataset C
1. Designing place based budgets
2. SYSTEM MODELLING
3. EVALUATION
KENT INTEGRATED DATASET
Data Quality Improvement
Benefits are:
better targeting of services
integrating services to tackle complex problems
stripping out the multiplicity of bodies and funding streams
cuts out waste and duplication.
What is the impact and benefit of individual services in the community versus all other services on population health?
Generating better assumptions to help predict demand and plan service capacity better
Activity
Staffing
Estates
Finance
Quality and safety

28. Some recent examples

31. For the population aged 75 and above, 67
For the population aged 75 and above, 67.4% had one or more QOF LTC, with 28.3% having 3 or more QOF LTCs.

32. Health Checks in Kent – Equity Audit
Females were significantly more likely to complete a health check when compared to males and there was moderate evidence that this inequity increases with age.
Mixed and multiple ethnic groups were significantly less likely to complete a health check when compared to the white ethnic category. This also the case in general terms for all other ethnic groups considered in the analysis.
ACORN segmentation analyses
Patients categorised as ‘Financially Stretched’ or ‘Urban Adversity’ were significantly less likely to complete a health check when compared to ‘Affluent Achievers’ and ‘Comfortable Communities’.
Similar findings include lesser uptake also seen for ‘Anxious Adversity’, ‘Poorly Pensioners’, Hardship Heartland’, ’Perilous Futures’ and ‘Struggling Smokers’.
- Very elevated smoking prevalence rates among patients completing a health check under ‘Urban Adversity’ and ‘Financially Stretched’.

33. Kent population model

34. The model interface and scenario generator
Changes in population health needs in response to prevention strategies  impact on service utilization rates

35. VERY COMPLEX and RESOURCE INTENSIVE
Key challenges faced
VERY COMPLEX and RESOURCE INTENSIVE
Information Governance – resource intensive
Data quality
Commissioner buy in
Influencing national policy
Limited capacity and resource availability
Changing governance arrangements

36. Discussion Points:
How can advanced analytical approaches and linked datasets be used to help improve population health outcomes?;
How can we work with senior decision makers on the use of data in decision making?